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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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theoretical models of how art-based methods can support both individual recovery and transformative organizational processes in social psychiatry. In terms of research The proposed PhD project are expected
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fabrication laboratories. Hands-on experience in surface engineering and scintillator detector testing. Training in modelling and simulation of radiation–matter interactions. Opportunities to present research
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. As part of this PhD, the candidate will: Conduct an integrative review of established competency models Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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Computing (e.g., memristor modeling/simulation/manufacturing) and Edge AI related areas (e.g., AI algorithms, AI accelerator, VLSI). Background Investigation Statement: Prior to hiring, the final candidate(s
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of materials · production of dressings and validation tasks · presentation of results and their dissemination PhD student 2 tasks: · performing numerical simulations